Creating the Hu-Int dataset: A comprehensive Arabic speech dataset for gender detection and age estimation of Arab celebrities

Speech is one of the attributes that humans enjoy. Humans possess several distinguishing characteristics, including fingerprints, hands, fingers, eyes and DNA, which set each individual apart from one another. Numerous datasets specialise in speech and include many languages from around the world, s...

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Bibliographic Details
Published in:Biomedical signal processing and control 2024-10, Vol.96, p.106511, Article 106511
Main Authors: Younis, Hussain A., Ruhaiyem, Nur Intan Raihana, Badr, Ameer A., Eisa, Taiseer Abdalla Elfadil, Nasser, Maged, Tan, Tien-Ping, Samsudin, Nur Hana, Salisu, Sani
Format: Article
Language:English
Subjects:
Age estimation
Dataset
Features
Gender detection
Speaker
Video captioning
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Summary:Speech is one of the attributes that humans enjoy. Humans possess several distinguishing characteristics, including fingerprints, hands, fingers, eyes and DNA, which set each individual apart from one another. Numerous datasets specialise in speech and include many languages from around the world, such as JNAS DATASET, TCDSA DATASET, UF-VAD DATASET, NIST SRE DATASET, a Gender Dataset, TIMIT dataset, common-voice dataset, SITW, VoxCeleb1, VoxCeleb2, Arabic-Saudi Arabic Speech Dataset-2 and MGB Challenge Dataset. This research contributes to the establishment of requirements for creating a new dataset featuring Arab celebrities in the Arabic language. The Hu-Int dataset is designed with the goal of profiling new notable Arab individuals. It includes 1017 speakers after the filtering process and 93.814 videos with various formulas (e.g. mp4, wav and CSV). The algorithms Random Forest, Logistic Regression, SVM (RBF), SVM (Linear), Decision Tree and CNN-LSTM are then used. The initial experimental results for algorithmic precision yielded the following accuracy rates: 0.88%, 0.87%, 0.96%, 0.77%, 0.85% and 0.88%, respectively, for males and 0.86%, 0.84%, 0.92%, 0.79%, 0.87% and 0.89%, respectively, for females in gender classification. The features of age were divided into six classes for gender classification. Dimensionality reduction was performed using principal component analysis and linear discriminant analysis. The results showed the opposite effect, with the SVM (RBF) and hybrid CNN-LSTM algorithms outperforming many other algorithms in both gender detection and age estimation.
ISSN:1746-8094
DOI:10.1016/j.bspc.2024.106511